Transcriptome analisys of bone marrow mononuclear cells derived from pediatric patients with acute myeloid leucosis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE84574 | GEO | 2016/07/20
SECONDARY ACCESSION(S): PRJNA329663
REPOSITORIES: GEO
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Project description:The aim of this research was to explore activation/deactivation of signaling pathways in mononuclear cells from pediatric patients with acute myeloid leucosis and acute lymphoid leucosis. Mononuclear cells were separated shortly after bone marrow samples collection. Cells were obtained by a density gradient centrifugation method using Ficoll. Cells were dissolved in RNALater solution, aliquoted and stored at -20°C till RNA extraction and microarray hybridisation.
2016-07-20 | E-GEOD-84574 | biostudies-arrayexpress
Project description:The aim of this research was to explore activation/deactivation of signaling pathways in mononuclear cells from pediatric patients with acute myeloid leucosis and acute lymphoid leucosis.
2016-07-20 | GSE84575 | GEO
Project description:The aim of this research was to explore activation/deactivation of signaling pathways in mononuclear cells from pediatric patients with acute myeloid leucosis and acute lymphoid leucosis. Mononuclear cells were separated shortly after bone marrow or blood samples collection. Cells were obtained by a density gradient centrifugation method using Ficoll. Cells were dissolved in RNALater solution, aliquoted and stored at -20°C till RNA extraction and microarray hybridisation.
2016-07-20 | E-GEOD-84575 | biostudies-arrayexpress
Project description:Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common form of leukemia in children. Multiple lncRNAs participate in normal and may be implicated in malignant hematopoiesis associated with blood cell cancers, such as leukemia. Currently, the expression profile of lncRNAs in pediatric AML is unclear. In this study, we evaluated the lncRNA expression profile in the tissue of three pediatric AML patients with lncRNA microarray techniques. In order to gain insight into the function of targets of lncRNAs, GO term and KEGG pathway annotation were applied to the target gene pool. qPCR was performed to evaluate the expression patterns of dys-regulated lncRNAs.
2015-12-31 | GSE65263 | GEO
Project description:The overall goal of this study was to profile pediatric acute lymphoblastic leukemia bone marrow tissue based on their transcriptome to characterize clinically relevant subtypes. This project has received funding from Cancer Society of Finland, Jane and Aatos Erkko Foundation and ERAPerMed (Reference Number: ERAPERMED2018-209) under the ERA-NET Cofund scheme of the Horizon 2020 Research and Innovation Framework Programme of the European Commission Research Directorate-General Grant Agreement No. 779282.
2023-04-05 | GSE228632 | GEO
Project description:Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common form of leukemia in children. Multiple lncRNAs participate in normal and may be implicated in malignant hematopoiesis associated with blood cell cancers, such as leukemia. Currently, the expression profile of lncRNAs in pediatric AML is unclear. In this study, we evaluated the lncRNA expression profile in the tissue of three pediatric AML patients with lncRNA microarray techniques. In order to gain insight into the function of targets of lncRNAs, GO term and KEGG pathway annotation were applied to the target gene pool. qPCR was performed to evaluate the expression patterns of dys-regulated lncRNAs. Bone marrow specimens were obtained at the time of diagnosis during routine clinical assessment of 3 pediatric patients with AML, who presented at the Department of Hematology and Oncology, Children's Hospital of Soochow University between 2000 and 2011. Additionally, bone marrow samples from 3 healthy donors were analyzed as controls. Human LncRNA Array analysis was performed by KangChen Bio-tech, Shanghai P.R. China. Total RNA from each sample was quantified by the NanoDrop ND-1000 and RNA integrity was assessed by standard denaturing agarose gel electrophoresis. For microarray analysis, Agilent Array platform was employed. The sample preparation and microarray hybridization were performed based on the manufacturerâs standard protocols with minor modifications. Briefly, mRNA was purified from total RNA after removal of rRNA (mRNA-ONLY⢠Eukaryotic mRNA Isolation Kit, Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 3â bias utilizing a random priming method. The labeled cRNAs were hybridized onto the Human LncRNA Array v2.0 (8 x 60K, Arraystar). After having washed the slides, the arrays were scanned by the Agilent Scanner G2505C. Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v12.0 software package (Agilent Technologies). After quantile normalization of the raw data, LncRNAs and mRNAs that at least 4 out of 6 samples have flags in Present or Marginal (âAll Targets Valueâ) were chosen for further data analysis. Differentially expressed LncRNAs and mRNAs with statistical significance between the two groups were identified through Volcano Plot filtering. Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show the distinguishable LncRNAs and mRNAs expression pattern among samples.
2015-12-31 | E-GEOD-65263 | biostudies-arrayexpress
Project description:Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining three published microarray datasets (PMIDs: 12086872, 12730115, 17002788) on 535 Caucasian samples and generating a new dataset on 100 Chinese children ALL samples, we were able to 1) identify a 62-gene classifier with 97.6% accuracy from the Caucasian samples and validated it on the completely independent set of 100 Chinese samples, 2) to uncover potential regulatory networks of ALL subtypes. The classifier we identified was so far the only one that could be applied directly to a single sample and sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL. A total of 100 Chinese pediatric acute lymphoblastic leukemia bone marrow (BM) samples were analyzed, together with five non-ALL BM samples as negative control (the five samples were combined to generate one control, see details in Li et al. Blood, 2009). The diagnosis of ALL was based on morphology, immunology, cytogenetic and molecular (MICM) classification. Cytogenetic ALL subtypes were identified experimentally by G-banding karyotype and multiplex nested RT-PCR. Among the 100 ALL patients, eleven relapsed within five years. All the samples, including those relapsed afterward, were from patients treated on BCH-2003 protocol and were extracted at their initial diagnosis. The five non-ALL BM samples were taken from the removed bones of patients who had plastic surgery for their bone deformity in Beijing Children’s Hospital. And the informed consent was obtained from parents, guardians, or patients (as appropriate). More details are available in Li et al. Gene expression-based classification and regulatory networks of pediatric acute lymphoblastic leukemia, Blood, 2009. We have generated these ALL and control samples to validate that a high accuracy subtype classifier that we identified could be applied directly to a single sample and sustained validation in a large independent patient group.
2009-08-19 | E-GEOD-17703 | biostudies-arrayexpress
Project description:This series represents leukemic samples obtained from pediatric AML patients at diagnosis and control normal bone marrow samples. Keywords: other
2005-01-28 | GSE2191 | GEO
Project description:The overall goal of this study was to characterize bone marrow cells based on their transcriptome, surface protein expression and BCR- and TCR VDJ-profile for accurate identification of clinically relevant cell states. This submission includes pediatric B-cell acute lymphoblastic leukemia samples. This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 824110 (EASI-Genomics)
2023-05-03 | GSE230295 | GEO
Project description:Pediatric acute myeloid leukemia (pedAML) is a heterogeneous blood cancer of childhood age. Although survival rates have significantly improved over the past decades, still 20-30% of children will succumb due to treatment-related toxicity or relapse. Molecular characterization of the leukemic stem cell, shown to be responsible for relapse, is needed to improve treatment options and survival. Recently, it became clear that non-coding RNAs, including long non-coding RNAs (lncRNA) and microRNA (miRNA), play a role in the development of human diseases, including pediatric cancer. Nevertheless, non-coding RNA expression data in pedAML are scarce. Here, we explored lncRNA and miRNA expression in pedAML subpopulations (leukemic stem cells (LSC) and leukemic blasts (L-blast)) and their normal counterparts (hematopoeitic stem cells (HSC) and control myeloblasts (C-blast)). The study provides a catalog of non-coding RNAs with a potential role in the pathogenesis of pedAML, paving the way for further translational research studies.
2022-08-16 | GSE196886 | GEO